Providing relevant and authentic channel content to users based on user persona and interest

ABSTRACT

A computer-implemented method comprising: receiving, by a computing device, content for a channel for publishing; determining, by the computing device, a measure of trustworthiness of the content; publishing, by the computing device, the content for the channel based on the measure of trustworthiness satisfying a threshold; generating, by the computing device, a channel persona profile for the channel based on the content associated with the channel and based on publishing the content; generating, by a computing device, a user persona profile for a user; determining, by the computing device, a match score indicating a level to which the channel persona profile matches the user persona profile; determining, by the computing device, channel discovery recommendation information based on the match score; and outputting, by the computing device, the channel discovery recommendation information.

BACKGROUND

The present invention generally relates to providing relevant andauthentic channel content to users and, more particularly, to providingrelevant and authentic channel content to users based on user personaand interest.

An internet-based media channel (or channel) may include a netcast orgroup of internet-accessible audio/video content. Channel content mayinclude an episodic series of digital audio or video files which a usercan download and listen to. Channel content may be available on asubscription basis, so that new episodes are automatically downloadedvia web syndication to the user's own local computer, mobileapplication, or portable media player. Subscriptions to channels mayrevolve around topics personalized to listener preferences, and may bestreamed or downloaded on demand. Channel content may be associated withsocial media platforms, and may be either transient or non-transient.Thousands or more audio artifacts related to internet-based mediachannels may be available for users to cater to users' varyinginterests. Social media-based channels may be formed by users of similarinterest across geographies to interact and enjoy a shared experiencesurrounding content of a particular topic with which the users share aninterest.

SUMMARY

In an aspect of the invention, a computer-implemented method includes:receiving, by a computing device, content for a channel for publishing;determining, by the computing device, a measure of trustworthiness ofthe content; publishing, by the computing device, the content for thechannel based on the measure of trustworthiness satisfying a threshold;generating, by the computing device, a channel persona profile for thechannel based on the content associated with the channel and based onpublishing the content; generating, by a computing device, a userpersona profile for a user; determining, by the computing device, amatch score indicating a level to which the channel persona profilematches the user persona profile; determining, by the computing device,channel discovery recommendation information based on the match score;and outputting, by the computing device, the channel discoveryrecommendation information

In an aspect of the invention: there is a computer program product fordetermining the authenticity of channel content and determining therelevancy of new channels to users. The computer program productincludes a computer readable storage medium having program instructionsembodied therewith, the program instructions executable by a computingdevice to cause the computing device to: receive content for a channelfor publishing; determine a measure of trustworthiness of the content;publish the content for the channel based on the measure oftrustworthiness satisfying a threshold; determining channel discoveryrecommendations for a user based on matching a plurality of channelpersona profiles with a user persona profile associated with the user;output the channel discovery recommendation information; and generate amemento personalized to the user, wherein the memento identifies pointsof interest during a session when the user access the content.

In an aspect of the invention, there is a system comprising: aprocessor, a computer readable memory and a computer readable storagemedium associated with a computing device; program instructions toreceive content for a channel for publishing; program instructions toextract audio characteristics of the content and video characteristicsof content, wherein the audio characteristics of the content comprise atranscription of the audio of the content, and the video characteristicsof the content comprise image analysis of video of the content; programinstructions to search one or more knowledge bases based on the audiocharacteristics and the video characteristics; program instructions togenerate a trustworthiness score based on the audio characteristics, andthe video characteristics, and the searching the one or more knowledgebases; and program instructions to publish the content for the channelbased on the trustworthiness score satisfying a threshold. The programinstructions are stored on the computer readable storage medium forexecution by the processor via the computer readable memory.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in the detailed description whichfollows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention.

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 shows an overview and environment of an example implementation inaccordance with aspects of the present invention

FIG. 5 shows an example flowchart of a process for providing channeldiscovery recommendations based on a match score indicating a level towhich a channel persona profile matches a user persona profile inaccordance with aspects of the present invention.

FIG. 6 shows an example flowchart of a process for generating a userpersona profile and using the user persona profile to generate and/ordiscover channels in accordance with aspects of the present invention.

FIG. 7 shows an example flowchart of a process for generating atrustworthiness score for a channel in accordance with aspects of thepresent invention.

FIG. 8 shows an example flowchart of a process for generating a channelpersona profile and generating discovery recommendations based on thechannel persona profile and user persona profiles in accordance withaspects of the present invention.

FIG. 9 shows an example flowchart of a process for generating a mementobased on the user's activity within a channel in accordance with aspectsof the present invention.

FIG. 10 illustrates an example of extracting sound characteristics,content features, and metadata features in accordance with aspects ofthe present invention.

FIG. 11 illustrates an example of extracting sound characteristics froman audio file in accordance with aspects of the present invention.

FIG. 12 illustrates an example of generating a personalized channelstate summary in accordance with aspects of the present invention.

FIG. 13 illustrates an example memento in accordance with aspects of thepresent invention.

DETAILED DESCRIPTION

The present invention generally relates to providing relevant andauthentic channel content to users and, more particularly, to providingrelevant and authentic channel content to users based on user personaand interest. Thousands or more audio artifacts related tointernet-based media channels are available for users to cater to users'varying interests. Social media-based channels are formed by users ofsimilar interest across geographies to interact and enjoy a sharedexperience surrounding content (e.g., transient or non-transientcontent) of a particular topic with which the users share an interest.However, a user interested in potentially subscribing to a channel mightnot know (a) if the channel is to their liking, and (b) if theactivities/topics of the channel are indeed what they claim to be. Forexample, the channel might be a spam or a fake to attract unsuspectingusers or the channel content may digress from the user's interest.Accordingly, aspects of the present invention authenticate the contentof a channel to verify that the channel is authentic with respect to thechannel's description and published attributes. Further, aspects of thepresent invention provide a list of channels in which a user isinterested to subscribe based on matching persona profiles of differentchannels with a persona profile of the user.

In embodiments, aspects of the present invention improve filtering andcontent suggestion techniques in the field of consumable online contentsharing. For example, unlike current systems, aspects of the presentinvention authenticate channels and provide guidance for generatingand/or modifying content (e.g., hosted on a content provider's platformor channel) based on feedback such as content trustworthiness, aggregatepersonality trait information of content subscribers, user interest,etc. As an illustrative, non-limiting example, aspects of the presentinvention provide feedback related to the trustworthiness of content, aswell as mathematical representations of the evolving aggregatepersonality traits and interests of users subscribed to the channel.This feedback provides guidance for modifying content to improve thepopularity of content to better align with the evolving interests andpersonalities of content consumers. Additionally, or alternatively, inembodiments, this feedback is used to improve content suggestions, thusimproving content popularity and user experience.

As described herein, aspects of the present invention generate andmaintain an up-to-date user persona profile based on the user'sinterests with regard to different types of content, social mediaactivity, user trustworthiness/ratings, user behavior data, etc. Aspectsof the present invention further generate and maintain an up-to-datechannel persona profile based on the attributes of content included inthe channel and the user persona profiles of users subscribed to thechannel. For example, the persona of the channel changes over time asthe content of the channel evolves and/or as different userssubscribe-unsubscribe from the channel. Aspects of the present inventionfurther generate a memento that captures the details of a user'sexperience when accessing a channel and/or interacting with other userswhile viewing/listening to channel content. The memento is used toupdate the user's persona profile with up-to-date information capturingthe user's interest. The updated persona profile for the user is used togenerate better matches for other channels with which the user isinterested.

As described herein, aspects of the present invention authenticate newchannels, identify and suggest relevant channels for which a user isinterested, maintain persona profiles for the user and the channel, andprovide updated channel recommendations as the user's personal profileand/or the persona profile of different channels change over time. Inthis way, users are given an opportunity to quickly identify a list ofchannels most relevant to them as the user's interests change over timeand as the persona of the channel changes over time (e.g., as thecontent of the channel changes over time and/or as the persona of theindividuals that subscribe and unsubscribe from the channel change overtime). Further, channel administrators may use channel persona profileinformation to modify the content of their channel to more closely matchthe persona profile of subscribed users (e.g., to retain existing users,attract additional users with a similar persona profile, etc.).

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a nonremovable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and channel authentication and discovering96.

Referring back to FIG. 1, the program/utility 40 includes one or moreprogram modules 42 that generally carry out the functions and/ormethodologies of embodiments of the invention as described herein (e.g.,such as the functionality provided by channel authentication anddiscovering 96). Specifically, the program modules 42 receive a requestto host content for a channel (e.g., transient or non-transientcontent), authenticate the content, generate and maintain user personaprofiles, generate and maintain channel persona profiles, match the userpersona profiles with the channel persona profiles to generate channelrecommendations, and generate a memento for a user session and use thememento to update the user's persona profile. Other functionalities ofthe program modules 42 are described further herein such that theprogram modules 42 are not limited to the functions described above.Moreover, it is noted that some of the modules 42 can be implementedwithin the infrastructure shown in FIGS. 1-3. For example, the modules42 are representative of a channel management system 215 shown in FIG.4.

FIG. 4 shows an overview and environment of an example implementation inaccordance with aspects of the present invention. As shown in FIG. 4,environment 400 includes a user device 210, a channel management system215, one or more external data servers 235, and a network 250. Inembodiments, one or more components in environment 400 may correspond toone or more components in the cloud computing environment of FIG. 2. Inembodiments, one or more components in environment 400 includes thecomponents of computer system/server 12 of FIG. 1.

The user device 210 includes a computer device capable of communicatingvia a network, such as the network 250. For example, the user device 210corresponds to a mobile communication device (e.g., a smart phone or apersonal digital assistant (PDA)), a portable computer device (e.g., alaptop or a tablet computer), a desktop computing device, a wearablecomputing device, or another type of device. The user device 210 is usedto access channels and content hosted by the channel management system215 and capture user behavior/biometrics data that indicates userinterest levels while viewing/listening to content from channels.

The channel management system 215 includes one or more computing devices(e.g., such as computer system/server 12 of FIG. 1). As further shown inFIG. 4, the channel management system 215 includes a user personaprofile component 220, a channel creation component 225, a channelauthentication component 230, a channel publication and discoverycomponent 240, and a memento creation component 245. The channelmanagement system 215 implements core processes of aspects of thepresent invention, as described herein. The external data server 235includes one or more computing devices (e.g., such as computersystem/server 12 of FIG. 1) that host web pages and/or other data thatis used by the channel management system 215 to verify/validate contentfrom one or more channels hosted by the channel management system 215.

The network 250 includes network nodes, such as network nodes 10 of FIG.2. Additionally, or alternatively, the network 250 includes one or morewired and/or wireless networks. For example, the network 250 includes acellular network (e.g., a second generation (2G) network, a thirdgeneration (3G) network, a fourth generation (4G) network, a fifthgeneration (5G) network, a long-term evolution (LTE) network, a globalsystem for mobile (GSM) network, a code division multiple access (CDMA)network, an evolution-data optimized (EVDO) network, or the like), apublic land mobile network (PLMN), and/or another network. Additionally,or alternatively, the network 250 includes a local area network (LAN), awide area network (WAN), a metropolitan network (MAN), the PublicSwitched Telephone Network (PSTN), an ad hoc network, a managed InternetProtocol (IP) network, a virtual private network (VPN), an intranet, theInternet, a fiber optic-based network, and/or a combination of these orother types of networks.

The quantity of devices and/or networks in the environment 400 is notlimited to what is shown in FIG. 4. In practice, the environment 400includes additional devices and/or networks; fewer devices and/ornetworks; different devices and/or networks; or differently arrangeddevices and/or networks than illustrated in FIG. 4. Also, in someimplementations, one or more of the devices of the environment 400 mayperform one or more functions described as being performed by anotherone or more of the devices of the environment 400. Devices of theenvironment 400 may interconnect via wired connections, wirelessconnections, or a combination of wired and wireless connections.

As further shown in FIG. 4, the user persona profile component 220provides user persona profiles to the channel creation component 225(e.g., at step 4.1). For example, the user persona profile component 220generates and maintains user persona profiles in which each user personaprofile identifies the user's interest, social mediareputation/trustworthiness, user behavior data, etc. Additional detailsregarding the generation of user persona profiles is discussed ingreater detail below with respect to FIG. 5.

The channel creation component 225 receives the user personal profilesand generates new channel content based on the user persona profiles(e.g., new content for an existing channel or a new channel altogether).In embodiments, the channel creation component 225 receives aninstruction to generate new channel content from a channel administratorin which the channel administrator uses information from the userpersona profiles (generated at step 4.1) and design a channel aroundcertain types of user personas. Additionally, or alternatively, thechannel creation component 225 automatically generates new channelcontent based popular user personas in which the channel creationcomponent 225 generates a channel having content that matches theinterests of users with a particular type of user persona. At step 4.2,the channel creation component 225 provides the new channel content tothe channel authentication component 230.

At step 4.3, the channel authentication component 230 communicates withone or more external data servers 235 to verify the authenticity of thenew channel content. For example, as described in greater detail withrespect to FIG. 6, the channel authentication component 230 extractsaudio and/or video data of the content, determines whether theinformation in the audio is accurate by transcribing the audio and/orapplying natural language processing to the audio, and verifies that theinformation discussed in the audio is accurate by communicating with theexternal data servers 235. Additionally, or alternatively, the channelauthentication component 230 may apply video/image analysis techniquesto determine whether the image described in the audio relates to what isshown in the video. In this way, spam related content and/or inaccuratecontent can be filtered out and prevented from being published.

At step 4.4, the channel authentication component 230 provides theverified channel content to the channel publication and discoverycomponent 240 for publishing the channel content. In embodiments, thechannel publication and discovery component 240 determines attributesand persona of the channel content. In aspects, the channel publicationand discovery component 240 maintains the persona of the channel contentover a period of time. At step 4.5, the channel publication anddiscovery component 240 provides, to a user device 210 that accesses thechannel management system 215, channel recommendations based on thepersona of channels hosted by the channel management system 215 andbased on the persona of the user associated with the user device 210.Further, as a user interacts within a channel (e.g., accessesaudio/video content, engages in discussions with other users), thememento creation component 245 tracks the user's interactions andgenerate a memento during an interactive session (e.g., at step 4.6). Inembodiments, memento is used to identify points/time indexes of interestfor content, generate a collage of the points of interest, used forrecollection to be referenced at a later time, and/or used to update theuser's persona profile and interests.

In embodiments, the user persona profile component 220 maintains andupdate the user's persona profile and the channel publication anddiscovery component 240 maintains and update the persona profile of achannel as more content to the channel is added and as users subscribeand unsubscribe from the channel. For example, the channel personaprofile is based on the attributes of content added or removed from thechannel over time, and/or the aggregate user persona profiles of thesubscribed users. As the channel persona profile evolves, the discoveryrecommendations provided by the channel publication and discoverycomponent 240 changes to stay up to date with a user's persona andinterest. Further, a channel administrator adjusts content accordinglyto more closely align the channel persona profile with that of thepersona profile of the users subscribed to the channel. In this way,user retention and channel popularity are improved.

FIG. 5 shows an example flowchart of a process for providing channeldiscovery recommendations based on a match score indicating a level towhich a channel persona profile matches a user persona profile. Thesteps of FIG. 5 are implemented in the environment of FIG. 5, forexample, and are described using reference numbers of elements depictedin FIG. 5. As noted above, the flowchart illustrates the architecture,functionality, and operation of possible implementations of systems,methods, and computer program products according to various embodimentsof the present invention.

As shown in FIG. 5, process 500 includes receiving content forpublishing (step 510). For example, the channel authentication component230 receives channel content for publishing from the channel creationcomponent 225 (e.g., by a user or channel administrator that uploads thecontent channel metadata, and description). Additional details regardingreceiving the content for publishing are described with respect toprocess step 710 in FIG. 7.

Process 500 further includes determining a measure of trustworthiness ofthe content (step 520). For example, the user persona profile component220 generates a trustworthiness score that indicates the level to whichthe channel content (e.g., received at step 510) is trustworthy (e.g.,authentic, not spam, etc.). Additional details regarding the determiningthe measure of trustworthiness are described in greater detail belowwith respect to process steps 720-760 in FIG. 7.

Process 500 also includes publishing content based on the measure oftrustworthiness satisfying a threshold (step 530). For example, the userpersona profile component 220 compares the trustworthiness score(determined at step 660) with a predefined and configurable threshold.The user persona profile component 220 publishes the content to thechannel publication and discovery component 240 when the trustworthinessscore satisfies the threshold, thus preventing untrustworthy contentfrom being published. Additional details regarding the determining themeasure of trustworthiness are described in greater detail below withrespect to process step 770 in FIG. 7.

Process 500 further includes generating a channel persona profile forthe channel based on the content associated with the channel (step 540).For example, the channel publication and discovery component 240generates a channel persona profile that includes a persona vector thatmodels the persona of a channel (e.g., a channel created at step 510)and the persona of users that are interested in subscribing to thechannel. Additional details regarding the generating the channel personaprofile are described in greater detail below with respect to processstep 810 in FIG. 8.

Process 500 also includes generating a user persona profile for a user(step 550). For example, the user persona profile component 220generates the user's persona profile based on trustworthiness of contentcontributed by the user, appropriateness of content contributed by theuser, the users' reputation, the user's interest, and/or otherinformation relating to the user's persona. Additional details regardingthe generating the user's persona profile are described in greaterdetail below with respect to process steps 610-660 in FIG. 6.

Process 500 further includes determining a match score indicating alevel to which the channel persona profile matches the user personaprofile (step 560). For example, the channel publication and discoverycomponent 240 matches the channel persona profile (e.g., generated atprocess step 540) to user persona profiles (e.g., generated at processstep 550) to generate a match score for each user persona profile. Thematch score is relatively higher when the details in the channel personavector more closely match the details in the user persona vector. Inother words, the match score measures the level to which the channelpersona profile matches the user persona profile. Additional detailsregarding the determining the match score are described in greaterdetail below with respect to process step 820 in FIG. 8.

Process 500 also includes determining and outputting channel discoveryrecommendations based on the match score (step 570). For example, thechannel publication and discovery component 240 outputs, for a user,channel discovery recommendations that identify channels that a user isinterested in subscribing. In embodiments, the channel discoveryrecommendations are a ranked list in order of match score (e.g., asdetermined at step 560). Additional details regarding the determiningand outputting channel discovery recommendations are described ingreater detail below with respect to process step 830 in FIG. 8.

FIG. 6 shows an example flowchart of a process for generating a userpersona profile and using the user persona profile to generate and/ordiscover channels. The steps of FIG. 6 are implemented in theenvironment of FIG. 6, for example, and are described using referencenumbers of elements depicted in FIG. 6. As noted above, the flowchartillustrates the architecture, functionality, and operation of possibleimplementations of systems, methods, and computer program productsaccording to various embodiments of the present invention.

As shown in FIG. 6, process 600 includes determining a trustworthinessof content contributed by the user (step 610). For example, the userpersona profile component 220 determines a trustworthiness of contentcontributed by the user (e.g., text in social media or publicconversations). In embodiments, the text is scanned for strong messages(e.g., containing nouns referring to a verifiable fact). In embodiments,subject, predicate, and/or object is extracted out of these strongmessages, and is verified against web knowledge bases (e.g., externaldata servers 235) for trustworthiness. The accuracy of the strongmessages is used to score trustworthiness of the content contributed bythe user

Process 600 also includes determining appropriateness of contentcontributed by user (step 620). In embodiments, appropriateness ofcontent refers to a level of civility, politeness, vulgarity, etc. Forexample, the user persona profile component 220 may analyze contentcontributed by user (e.g., comments, messages, etc. in a social mediasetting). In embodiments, the user persona profile component 220performs sentiment analysis, tone analysis, and/or other type of naturallanguage processing to determine the aggregate appropriateness of all ofthe user's contributed content. In embodiments, the user persona profilecomponent 220 may compare the text with a database of blacklistedtext/topics and determine a measure of appropriateness based on thenumber of blacklisted words/topics included in the user's contributedcontent.

Process 600 further includes obtaining user reputation data (step 630).For example, the user persona profile component 220 obtains userreputation data with respect to other users using scoring techniquessuch as mutual user ratings.

Process 600 also includes scoring values to add to the user's personavector (step 640). For example, the user persona profile component 220scores values related to the user's trustworthiness (from step 610), theuser's appropriateness (from step 620), the user's reputation (from step630), and/or other values for other factors. The scored values areincorporated into a user persona vector as described herein.

Process 600 further includes generating the user's persona profile withthe persona vector (step 650). For example, the user persona profilecomponent 220 generates the user's persona profile with the personavector. In embodiments, the persona vector contains fields which areselected from the similarity in the individual user characteristics suchas mood, physical location, ambience (scenes, monuments, route, noiselevel etc.), activity (walking, sitting, jogging, driving, commuting,sleeping etc.). Intent expressed in physical and messenger conversationsare mapped to popular topics mined from knowledge bases (e.g., hosted bythe external data servers 235). In embodiments, the persona vector alsoidentifies the user's interests as determined using any suitabletechnique.

Process 600 also includes converting the persona vector into a neuralembedding (step 660). For example, the user persona profile component220 implements an auto-encoder to learn a representation for the abovefeatures derived at steps 610-650, such as trustworthiness of content,appropriateness of the content, user reputation data, user personavector, and/or user person profile. In embodiments, the auto-encoderlearns a representation for the features embedded in a unique signatureof each use or session accessing a channel hosting the content. Inembodiments, the auto-encoder is an artificial neural network thatlearns a compressed, distributed representation of the features. Therepresentations are learned in such a way that the they are able toreconstruct the original features. This self-reconstruction techniqueaids in deriving meaningful representations for a given dataset. Onceusers are represented in this basic neural embedding representation,similar users having high similarity in the derived features will lienear to each other in an N-dimensional space model. Given a specifictrust requirement (e.g., a score threshold), users within theN-dimensional clusters within the model who do satisfy the threshold arefiltered out.

Process 600 further includes outputting the user persona profile forgenerating and/or discovering channels (step 670). For example, the userpersona profile component 220 outputs the user persona profile and addthe user persona profile to a collection of multiple user personaprofiles. This collection is used to generate new channel content tomatch the persona of a collection of user personas of a particular type.Additionally, or alternatively, a user persona profile is used todiscover channels for a user that the user is interested (e.g., based ona degree to which the user's persona profile matches the channel'spersona profile).

FIG. 7 shows an example flowchart of a process for generating atrustworthiness score for a channel. The steps of FIG. 7 are implementedin the environment of FIG. 4, for example, and are described usingreference numbers of elements depicted in FIG. 4. As noted above, theflowchart illustrates the architecture, functionality, and operation ofpossible implementations of systems, methods, and computer programproducts according to various embodiments of the present invention.

As shown in FIG. 7, process 700 includes receiving channel content,channel metadata, and description (step 710). For example, the channelauthentication component 230 receives channel content for publishing,metadata for the content, and description of the content from thechannel creation component 225 (e.g., by a user or channel administratorthat uploads the content channel metadata, and description). Asdescribed herein, a channel administrator creates a channel using userpersona profiles (e.g., user persona profiles generated at process step650). When creating the channel, the channel administrator inputsmetadata and a description of the channel.

Process 700 also includes extracting metadata, audio, and videocharacteristics of channel content (step 720). For example, the userpersona profile component 220 extracts audio and video characteristicsof the channel content (e.g., from step 710) using beats pattern usinghit point analysis, frequency patterning using spectrum analysis,amplitude pattern using loudness analysis, or the like. In embodiments,a unique signature for the channel content is created and stored usingthe extract characteristics.

In embodiments, content and metadata features are extracted from thecontent. For example, extracting content features includes classifyingthe audio content into classes using any suitable classificationtechnique. Examples include speeches, lectures, discussions, songsinstrumentals, animal sounds etc. In embodiments, the lyrics of thecontent are transcribed and tokenized to identify features such asstrong words, conversations, topics, speaker identities (if available),mood of the content, nature of the content, etc. Further, a feature setis constructed from the extracted content features. In embodiments,metadata features are also extracted. Metadata features include textualinformation mined from the comments, transcripts, accompanying slidesviewer feedback (like/dislike), views, audio annotations etc. Inembodiments, metadata is parsed to extract the nature of the content,its popularity across demographics and interest groups. In embodiments,the feature sets from content and metadata features are combined withthe sound characteristic features to create a feature vector.

Process 700 further includes transcribing the extracted audio (step730). For example, the user persona profile component 220 transcribesthe extracted audio using any suitable audio transcription technique toidentify the lyrics and/or words of the audio. As described herein, thelyrics of the content is transcribed and tokenized to identify featuressuch as strong words, conversations, topics, speaker identities (ifavailable), mood of the content, nature of the content, etc.

Process 700 also includes performing image analysis on the extractedvideo (step 740). For example, the user persona profile component 220performs image analysis on the extracted video using any suitable imageanalysis technique (e.g., pixel-based classification, object detection,etc.). As described herein, the extracted video is used to determine ifthe video content is relevant to the lyrics and/or words of the audio(e.g., obtained at step 730) and/or the channel metadata/description(e.g., obtained at step 710).

Process 700 further includes searching external servers for relevantdata (step 750). For example, the user persona profile component 220searches the external data servers 235 (e.g., knowledge bases, messageboards, etc.) for relevant data associated with the extracted audioand/or video. In embodiments, the user persona profile component 220uses any suitable searching system in which search queries includeswords from the transcribed audio and/or references to objects,individuals, etc. identified from the extracted video. As describedherein, the user persona profile component 220 searches the externaldata servers 235 to determine whether the audio and video is related toeach other, and to determine whether the information in the content isaccurate (e.g., trustworthy).

Process 700 also includes generating a trustworthiness score bycomparing the audio characteristics, video characteristics, metadata,and/or external data (step 760). For example, the user persona profilecomponent 220 generates a trustworthiness score that indicates the levelto which the channel content is trustworthy (e.g., authentic, not spam,etc.). In embodiments, the user persona profile component 220 generatesthe trustworthiness score by comparing the audio characteristics (e.g.,transcribed audio), video characteristics (e.g., identified individuals,objects, subjects, etc. from the video), metadata, and/or external data(e.g., knowledge bases). As an example, the user persona profilecomponent 220 generates a relatively high trustworthiness score when theaudio transcription matches the content in the video, the metadata, andhas accurate information closely corresponding information fromknowledge base. As another example, the user persona profile component220 generates a relatively low trustworthiness score when the audiotranscription does not match the content in the video, the metadata, andhas inaccurate information closely corresponding information fromknowledge base.

Process 700 further includes outputting the channel content to thechannel publication and discovery component based on the trustworthinessscore satisfying a threshold (step 770). For example, the user personaprofile component 220 compares the trustworthiness score (determined atstep 760) with a predefined and configurable threshold. The user personaprofile component 220 publishes the content to the channel publicationand discovery component 240 when the trustworthiness score satisfies thethreshold, thus preventing untrustworthy content from being published.

FIG. 8 shows an example flowchart of a process for generating a channelpersona profile and generating discovery recommendations based on thechannel persona profile and user persona profiles. The steps of FIG. 8are implemented in the environment of FIG. 4, for example, and aredescribed using reference numbers of elements depicted in FIG. 4. Asnoted above, the flowchart illustrates the architecture, functionality,and operation of possible implementations of systems, methods, andcomputer program products according to various embodiments of thepresent invention.

As shown in FIG. 8, process 800 includes generating a channel personaprofile based on the channel content attributes and user personaprofiles of subscribed users (step 810). For example, the channelpublication and discovery component 240 generates a channel personaprofile that includes a persona vector that models the persona of achannel (e.g., a channel created at step 610) and the persona of usersthat are interested in subscribing to the channel. As described herein,the channel persona profile is based on attributes/characteristics ofthe content associated with the channel (e.g., the metadata/descriptionof the content, trustworthiness of the content, content subjects, typesof content, user discussions within the channel regarding the content,etc.). In embodiments, the attributes/characteristics of the contentassociated with the channel are obtained (e.g., using process steps720-740) and stored in a feature vector for the content. In embodiments,the feature vector is used to generate the channel persona profile.Additional details regarding the feature vector are described in greaterdetail with respect to FIG. 10. In embodiments, the channel personaprofile is further based on the user persona profiles of users currentlysubscribed to the channel. For example, the channel persona profilesincludes an aggregate or weighted average of the personas and/orattributes from the persona vectors of the users subscribed to thechannel. Additionally, or alternatively, the channel persona profile isfurther based on persona profiles of target users that a channeladministrator wishes to attract.

Process 800 also includes matching the channel persona profile with userpersona profiles (step 820). For example, the channel publication anddiscovery component 240 matches the channel persona profile to userpersona profiles (e.g., generated at step 650) to generate a match scorefor each user persona profile. The match score is relatively higher whenthe details in the channel persona vector more closely match the detailsin the user persona vector. In other words, the match score measures thelevel to which the channel persona profile matches the user personaprofile.

Process 800 further includes generating and outputting channel discoveryrecommendations based on the matching (step 830). For example, thechannel publication and discovery component 240 outputs, for a user,channel discovery recommendations that identify channels that a user isinterested in subscribing. In embodiments, the channel discoveryrecommendations are a ranked list in order of match score.

Process 800 also includes generating and outputting a channel statesummary (step 840). For example, the channel publication and discoverycomponent 240 generates a channel state summary that includes a summaryof channel content featured within the channel and user discussionsregarding the content. The channel state summary includes informationthat assists a user in deciding whether the user wishes to subscribe tothe channel.

As an illustrative, non-limiting example, the channel state summaryidentifies that the channel largely includes violin tunes as opposed toconversation between members about violins. A user may consider joiningor subscribing to the channel because the user is interested inlistening to music featuring violins. On the other hand, if the channelcontent is dominated by violin lesson instructions, then the user maynot be interested in the channel. In this way, the channel state summaryassists the user to decide whether the user wishes to join the channel,and whether the user wishes to and quickly participate in the channelactivity. The channel state summary identifies the match level of thechannel with the user. In this particular example, the channel statesummary identifies the proportion of conversations to tunes.

In embodiments, the personalized channel state summary is generated bymodeling the conversations and audio of the content for topics matchingthe user's persona profile. If such topics exist, then the conversationsrelated to that are sampled for the summary. Additionally, oralternatively, the audio content features (e.g., obtained at step 620)are also added to the channel state summary. A trust score is generatedfrom the users of the channel and their activity and included in thechannel state summary.

Process 800 also includes updating the channel persona profile based onuser persona profiles as users subscribe and unsubscribe (step 850). Forexample, the channel publication and discovery component 240 updates thechannel persona profiles by re-generating the channel persona profilewith consideration to the persona vectors of the user persona profilesof those users who have recently subscribed to the channel, whiledeleting the data from the persona vectors of users that haveunsubscribed to the channel.

Process 800 further includes updating the channel discoveryrecommendations based on the updated channel persona profile (step 860).For example, the channel publication and discovery component 240generates updated channel discovery recommendations by regenerating thechannel discovery recommendations after updating the channel personaprofile. In this way, the channel discovery recommendations are updatedas the channel's persona evolves over time. Additionally, oralternatively, a channel administrator adjusts the content of thechannel to more closely align with a desired channel persona and toretain and/or attract users of a particular persona.

FIG. 9 shows an example flowchart of a process for generating a mementobased on the user's activity within a channel. The steps of FIG. 9 areimplemented in the environment of FIG. 4, for example, and are describedusing reference numbers of elements depicted in FIG. 4. As noted above,the flowchart illustrates the architecture, functionality, and operationof possible implementations of systems, methods, and computer programproducts according to various embodiments of the present invention.

As shown in FIG. 9, process 900 includes monitoring user activity whilea user access channel content during a session (step 910). For example,the memento creation component 245 monitors various activities of theuser. In embodiments, the memento creation component 245 monitors thevarious user activities by periodically receiving and/or storing datafrom the user device 210, such as data from wearable computing devices,camera devices, microphone devices relating to conversations about thecontent, etc.) during a session in which the user is accessing channelcontent (e.g., an IP-based session in which the user has selected toplay back the channel content).

Process 900 also includes identifying points of interest in the content(step 920). For example, the memento creation component 245 identifiesuser activity that is directly of interest to the user (e.g., the user'sconversation, gestures) or indirectly of interest to the user (e.g.,other users speaking about a topic in which the user is interested)while the user is listening to, viewing, and/or otherwise accessing thecontent (e.g., from step 910).

Process 900 further includes generating and storing a meta-file based onuser's points of interest (step 930). For example, the memento creationcomponent 245 generates a personalized memento including a meta-fileaccompanying the content. In embodiments, the meta-file is personalizedwith respect to the user's persona profile and participation/activityincluding the trustworthiness/influence score of the responses fromother users. Additionally, or alternatively, the meta-file containsreferences to the content. In embodiments, the meta-file is used as adata store which is referenced and relevant data retrieved whenever theuser makes a reference to a topic identified in the memento in laterconversations or interactions.

Process 900 also includes generating a collage using the meta-file data(step 940). For example, the memento creation component 245 generates anaudio/video collage with the conversations, gestures and interactions,In embodiments, the audio/video collage includes visuals identifying theuser's points of interest (from step 930) and an audio track whichincludes the audio within the content (e.g., the content at step 910).

FIG. 10 illustrates an example of extracting sound characteristics,content features, and metadata features in accordance with aspects ofthe present invention. In embodiments, the user persona profilecomponent 220 extracts audio sound characteristics 1002, audio contentfeatures 1004, and audio metadata features 1006 from audio content(e.g., audio content from a channel produced by the channel creationcomponent 225 for example, at step 710) for example, in a similar manneras described above with respect to process step 720 in FIG. 7. As shownin FIG. 10, audio sound characteristics 1002, audio content features1004, and audio metadata features 1006 are extracted to form a featurevector 1008 in which the feature vector 1008 identifies, in a structuredmanner, the sound characteristics, content features, and metadatafeatures. In embodiments, the feature vector 1008 is used for channeldiscovery and/or generating trustworthiness score of the channel contentas described above with respect to process 700 in FIG. 7. Inembodiments, the feature vector 1008 corresponds to the feature vectordescribed at step 720.

FIG. 11 illustrates an example of extracting sound characteristics froman audio file in accordance with aspects of the present invention. Inembodiments, the user persona profile component 220 extracts the soundcharacteristics of audio content 1102 produced by the channel creationcomponent 225 in a similar manner as described above with respect tostep 620 (e.g., to generate a feature vector as described above withrespect to FIG. 9). Additionally, or alternatively, the user personaprofile component 220 extracts the sound characteristics of an audiofile in a user's library (e.g., as part of determining the user'spersona vector based on the sound characteristics of audio files in theuser's library). As shown in FIG. 11, sound characteristics areextracted using hit point analysis 1104 (e.g., beats analysis), spectrumanalysis 1106 (e.g., frequency analysis), loudness analysis 1108 (e.g.,amplitude analysis), etc. In embodiments, the frequency and range areplotted and incorporated as part of the user's persona vector (e.g., asshown in graph 1110). In embodiments, the sound characteristics arestored in the user's persona vector and/or stored in another locationfor future use.

FIG. 12 illustrates an example of generating a personalized channelstate summary in accordance with aspects of the present invention. Inembodiments, the personalized channel state summary is generated usingthe techniques in FIG. 12 in addition to or instead of those techniquesdescribed above with respect to FIG. 7 (e.g., at step 740). As shown inFIG. 12, in an example embodiment, user conversation data (e.g.,messenger conversations 1202, audio files in the user's librarycontaining speech 1204, user's live or physical conversations 1206,etc.) is converted into raw data and provided to a conversationsegmenter 1208. In embodiments, the conversation segmenter 1208 segmentsthe user conversation data to form segmented conversation data 1210. Theuser's persona vector 1212 (e.g., generated at step 650 in FIG. 6) andsegmented conversation data 1210 are used as inputs into a topic modeler1214 that models a probability that the conversations are associatedwith a particular topic. In embodiments, a sentiment analyzer 1216identifies the sentiment of each topic and the sentiment of topics isalso used as an input to the topic modeler 1214. In embodiments, fromthe content feature vector 1218 (e.g., the feature vector 908 of FIG. 9)and the data produced by the topic modeler 1214, the personalizedchannel state summary 1220 is produced (e.g., as described at step 740)to identify suggested channels for the user.

FIG. 13 illustrates an example memento in accordance with aspects of thepresent invention. As shown in FIG. 13, an in accordance with an exampleembodiment, the memento 1300 shows an audio print of the user'sinteractions during playback of content audio. For example, the memento1300 shows time indexes and audio prints associated with the user'sconversations, times when the user applauds, makes a thumbs up gesture,or appears to be drowsy (e.g., by monitoring user activity at step 810).In this way, the memento is used to identify points/time indexes ofinterest for content, generate a collage of the points of interest, usedfor recollection to be referenced at a later time, and/or used to updatethe user's persona profile and interests. In embodiments, the memento1300 is displayed in a user interface. In embodiments, the memento 1300is interactive and allows the user to select the points of interest tojump to the points of interest when replaying the content.

In embodiments, a service provider could offer to perform the processesdescribed herein. In this case, the service provider can create,maintain, deploy, support, etc., the computer infrastructure thatperforms the process steps of the invention for one or more customers.These customers are, for example, any business that uses technology. Inreturn, the service provider can receive payment from the customer(s)under a subscription and/or fee agreement and/or the service providercan receive payment from the sale of advertising content to one or morethird parties.

In still additional embodiments, the invention provides acomputer-implemented method, via a network. In this case, a computerinfrastructure, such as computer system/server 12 (FIG. 1), can beprovided and one or more systems for performing the processes of theinvention can be obtained (e.g., created, purchased, used, modified,etc.) and deployed to the computer infrastructure. To this extent, thedeployment of a system can comprise one or more of: (1) installingprogram code on a computing device, such as computer system/server 12(as shown in FIG. 1), from a computer-readable medium; (2) adding one ormore computing devices to the computer infrastructure; and (3)incorporating and/or modifying one or more existing systems of thecomputer infrastructure to enable the computer infrastructure to performthe processes of the invention.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a computing device, content for a channel for publishing;determining, by the computing device, a measure of trustworthiness ofthe content comprising: transcribing audio of the content to determinewords of the audio; performing image analysis of video of the content todetermine whether or not the video is relevant to the words of theaudio; and generating a trustworthiness score based on the determiningwhether or not the video is relevant to the words of the audio and acomparison comparing metadata of the content and external dataassociated with the content; publishing, by the computing device, thecontent for the channel based on the measure of trustworthinesssatisfying a threshold; generating, by the computing device, a channelpersona profile for the channel based on the content associated with thechannel and based on publishing the content; generating, by a computingdevice, a user persona profile for a user; determining, by the computingdevice, a match score indicating a level to which the channel personaprofile matches the user persona profile; determining, by the computingdevice, channel discovery recommendation information based on the matchscore; and outputting, by the computing device, the channel discoveryrecommendation information.
 2. The computer-implemented method of claim1, wherein the measure of trustworthiness comprises a measure ofaccuracy of the transcribed audio with respect to one or more knowledgebases.
 3. The computer-implemented method of claim 1, wherein generatingthe user persona profile comprises: determining a level of usertrustworthiness based on content contributed by the user; determining alevel of the user's appropriateness based on the content contributed bythe user; determining the user's interests; and generating a personavector identifying the user's trustworthiness, the user'sappropriateness and the user's interests.
 4. The computer-implementedmethod of claim 3, wherein generating the user persona profile furthercomprises converting the persona vector into a neural embedding.
 5. Thecomputer-implemented method of claim 1, wherein the generating thechannel persona profile is based on attributes of the content associatedwith the channel and a plurality of user persona profiles for aplurality of users subscribed to the channel.
 6. Thecomputer-implemented method of claim 1, further comprising generating amemento personalized to the user, wherein the memento identifies pointsof interest during a session when the user access the content.
 7. Thecomputer-implemented method of claim 6, wherein the memento furthercomprises an audio/video collage with the conversations, gestures, andinteractions associated with the user during the session.
 8. Thecomputer-implemented method of claim 1, wherein a service provider atleast one of creates, maintains, deploys and supports the computingdevice.
 9. The computer-implemented method of claim 1, wherein thereceiving the content for a channel for publishing, the determining themeasure of trustworthiness of the content, the publishing, thegenerating the channel persona profile, the generating the user personaprofile, the determining the match score, the determining the channeldiscovery recommendation, and the outputting the channel discoveryrecommendation are provided by a service provider on a subscription,advertising, and/or fee basis.
 10. The computer-implemented method ofclaim 1, wherein the computing device includes software provided as aservice in a cloud environment.
 11. The computer-implemented method ofclaim 1, further comprising deploying a system for determining theauthenticity of channel content and determining the relevancy of newchannels to users, comprising providing a computer infrastructureoperable to perform the receiving the content for a channel forpublishing, the determining the measure of trustworthiness of thecontent, the publishing, the generating the channel persona profile, thegenerating the user persona profile, the determining the match score,the determining the channel discovery recommendation, and the outputtingthe channel discovery recommendation.
 12. A computer program product,the computer program product comprising a computer readable storagemedium having program instructions embodied therewith, the programinstructions executable by a computing device to cause the computingdevice to: receive content for a channel for publishing; determine ameasure of trustworthiness of the content comprising: transcribing audioof the content to determine words of the audio; performing imageanalysis of video of the content to determine whether or not the videois relevant to the words of the audio; and generating a trustworthinessscore based on the determining whether or not the video is relevant tothe words of the audio and a comparison comparing metadata of thecontent and external data associated with the content; publish thecontent for the channel based on the measure of trustworthinesssatisfying a threshold; determine channel discovery recommendations fora user based on matching a plurality of channel persona profiles with auser persona profile associated with the user; output the channeldiscovery recommendation information; and generate a mementopersonalized to the user, wherein the memento identifies points ofinterest during a session when the user access the content.
 13. Thecomputer program product of claim 12, wherein the memento furthercomprises an audio/video collage with the conversations, gestures, andinteractions associated with the user during the session.
 14. Thecomputer program product of claim 12, wherein the user persona profileis based on a level of user trustworthiness based on content contributedby the user, a level of the user's appropriateness based on the contentcontributed by the user, and the user's interests.
 15. A systemcomprising: a processor, a computer readable memory and a computerreadable storage medium associated with a computing device; programinstructions to receive content for a channel for publishing; programinstructions to extract audio characteristics of the content includingtranscribing audio of the content to determine words of the audio, andvideo characteristics of content including performing image analysis ofvideo of the content to determine whether or not the video is relevantto the words of the audio; program instructions to search one or moreknowledge bases based on the audio characteristics and the videocharacteristics; program instructions to generate a trustworthinessscore based on the determining whether or not the video is relevant tothe words of the audio, and the searching the one or more knowledgebases; and program instructions to publish the content for the channelbased on the trustworthiness score satisfying a threshold, wherein theprogram instructions are stored on the computer readable storage mediumfor execution by the processor via the computer readable memory.
 16. Thesystem of claim 15, wherein the trustworthiness score indicates ameasure of accuracy of the transcription with respect to the one or moreknowledge bases.
 17. The computer-implemented method of claim 1, furthercomprising extracting audio and video characteristics of the contentusing hit point analysis, frequency patterning using spectrum analysis,and amplitude pattern using loudness analysis.
 18. Thecomputer-implemented method of claim 17, further comprising tokenizingthe words of the audio to identify strong words, conversations, topics,speaker identities, mood of the content, and nature of the content. 19.The computer-implemented method of claim 18, wherein the image analysiscomprises pixel-based classification and object detection.
 20. Thecomputer-implemented method of claim 19, wherein the generating thetrustworthiness score is further based on the audio and videocharacteristics of the content extracted using the hit point analysis,the frequency patterning using spectrum analysis, and the amplitudepattern using loudness analysis.